Resumo

SINGH, Vijendra Pratap; PANDEY, Manish Kumar; SINGH, Pangambam Sendash  e  KARTHIKEYAN, Subbiah. An LSTM Based Time Series Forecasting Framework for Web Services Recommendation. Comp. y Sist. [online]. 2020, vol.24, n.2, pp.687-702.  Epub 04-Out-2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-2-3402.

The convergence of Social Mobility Analytics and Cloud (SMAC) technologies gives rise to an unforeseen aggrandization of the web services on the internet. The resilience and payment-based approach of the cloud makes it an obvious choice for the deployment of web services-based applications. Out of available web services, to gratify the similar functionalities, the choice of the web service based on the personalized quality of service (QoS) parameters plays an important role in determining the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the non-functional performance of web services correlated behavior with the invocation time. This happens because service status usually changes over time. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and becomes a challenging research issue. In this study, LSTM based deep learning models were used for the prediction of these time aware QoS parameters and the results are compared with the previous approaches. The experimental results show that the LSTM based Time Series Forecasting Framework is performing better. The RMSE, MAE, and MAPE are used as an evaluation metric and their value for the prediction of Response time (RT) is found to be 0.030269, 0.02382 and 0.59773 respectively with adaptive moment estimation as the training option and is found to be 0.66988, 0.66465 and 27.9934 respectively with root mean square propagation as the training option. The RMSE, MAE, and MAPE value for the prediction of throughput (TP) is found to be 0.77787, 0.4792 and 1.61 respectively with adaptive moment estimation as the training option and is found to be 0.2.7087, 1.4076and 7.1559 respectively with root mean square propagation as the training option respectively. Thus, the experimental results show that the LSTM model of Time Series Forecasting for Web Services Recommendation Framework is performing better as compared to previous methods.

Palavras-chave : Time-Aware web services recommendation; QoS-Prediction; LSTM; SMAC; cloud services.

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